# Each team member would need to set their directory path to variable "project_dir"
# we will make sure everything else is relative to "project_dir"
project_dir <- "/home/admin-12/Documents/IMARTICUS/Data-Riders/EDA"
setwd(project_dir)
# Setup Packages
load_packages <- function () {
# Imports
packages <- c("VIM", "dplyr", "plotly", "psych", "corrplot", "cluster", "factoextra", "psych", "tm", "wordcloud2")
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
install.packages(packages[!installed_packages])
}
# Packages loading with suppressed messages
suppressMessages(invisible(lapply(packages, library, character.only = TRUE)))
}
load_packages()
# Get the Raw Data from the file
read_file <- function () {
data <- read.csv("data/cereals_data.csv")
return (data)
}
# Handle missing values
handle_missing_values <- function (input) {
output <- input
output <- kNN(output)
return (output)
}
# Clearn the data to make it good for processing
# eg. delete unwanted rows, change data types etc
data_processing <- function (input) {
output <- input
output <- handle_missing_values(output)
# Set company names
output$mfr_names <- as.character(output$mfr)
output$mfr_names[output$mfr_names=="A"] <- "American Home Food Products"
output$mfr_names[output$mfr_names=="G"] <- "General Mills"
output$mfr_names[output$mfr_names=="K"] <- "Kelloggs"
output$mfr_names[output$mfr_names=="N"] <- "Nabisco"
output$mfr_names[output$mfr_names=="P"] <- "Post"
output$mfr_names[output$mfr_names=="Q"] <- "Quaker Oats"
output$mfr_names[output$mfr_names=="R"] <- "Ralston Purina"
output$mfr_names_factor = as.factor(output$mfr_names)
# Calorie Categories
output <- within(output, {
calories_category <- NA
calories_category[calories>=50&calories<80] <- "L"
calories_category[calories>=80&calories<110] <- "M"
calories_category[calories>=110] <- "H"
})
# some data processing steps that would be required
return (output)
}
# Get the Data Frame for the data
get_data <- function () {
raw <- read_file()
data <- data_processing(raw)
return (data)
}
data <- get_data()
data_grp_mfr <- data %>% group_by(mfr_names_factor)
Part 1: Understanding Variables
Overall View
draw_density_plot <- function (value_set, xlab) {
skewness <- round(skew(value_set), 2)
mean <- round(mean(value_set), 2)
median <- round(median(value_set), 2)
title <- paste(xlab, "Density Chart | ", "Skewness:", skewness)
plot(density(value_set), ylab = "Probabilty Density", xlab = xlab, main=title,)
abline(v=mean, col="red")
abline(v=median, col="blue")
}
Distribution of Calories (in the Products)
draw_density_plot(data$calories, "Calories")

Distribution of Ratings (for the Products)
draw_density_plot(data$rating, "Rating")

Part 2: Data Insights
Distribution of Rating vs Calorie Category
data %>% ggplot(aes(x = rating, fill = calories_category)) + geom_histogram() + scale_fill_brewer(palette = "Set1") +
scale_x_continuous(name = "Ratings", expand = c(0,0)) +
labs(fill = " Cal ", y ='Count', title = "Distribution of Rating vs Calorie Category", subtitle = "Higher the calories, Lower is the Rating") +
theme_classic()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Relationship of one variable with another
cor_result <- cor((data_grp_mfr %>% select_if(is.numeric))[-1])
corrplot(cor_result)

heatmap(cor_result)

good_bad_corr <- function (cor_result_filter) {
#cor_result_filter <-
cor_result_filter_names <- names(cor_result_filter)
names(cor_result_filter) <- NULL
cor_result_df <- data.frame(cor_result_filter_names, cor_result_filter) %>% arrange(cor_result_filter)
names(cor_result_df) <- c("Parameters", "Corellation")
ggplot(cor_result_df, aes(x=Parameters, y=Corellation)) + geom_bar(stat = "identity") +
theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))
}
Influencers for Ratings
good_bad_corr(cor_result["rating",])

Influencers for Calories
good_bad_corr(cor_result["calories",])

Whats you get in one Serving
data %>% ggplot(aes(x = cups, fill = mfr_names)) +
geom_histogram() +
scale_x_continuous(name = "# of Cups", expand = c(0,0)) +
labs(fill = "Manufacturer", title = "Distribution of Cups per Serving",
subtitle = "different cups for servings", y="# of Products") +
theme_classic() + scale_fill_brewer(palette="Dark2")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

data %>% ggplot(aes(x = weight, fill = mfr_names)) + geom_histogram() +
scale_x_continuous(name = "Weight (in ounces)", expand = c(0,0)) +
labs(fill = "Manufacturer", title = "Distribution of Weight per Serving", subtitle = "Weight per serving") +
theme_classic() + scale_fill_brewer(palette="Dark2")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Best in the Market
d <- data %>% arrange(rating) %>% head(4) %>% select(name, rating)
ggplot(data=d, aes(x=name, y=rating)) + geom_bar(stat = "identity") +
labs(y="Ratings") + scale_fill_brewer(palette="Dark2") +
theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))

Part 3: Analysis by company
data_grp_mfr %>% plot_ly(y= ~rating, x= ~mfr_names_factor, type = "box", color= ~mfr_names_factor) %>%
layout(title="Company wise Rating Consistency", xaxis=list(title="Manufactures"), yaxis=list(title="Rating"))
Part 4: Overall Picture
Word Cloud (error in markdown)
get_file_content <- function () {
txt <- readLines("/home/admin-12/Documents/IMARTICUS/Data-Riders/EDA/data/health.txt")
txt <- paste(txt, collapse = " ")
txt <- tolower(txt)
txt <- gsub("[^a-zA-Z]", " ", txt)
txt <- gsub("\\s+", " ", txt)
txt <- removeWords(txt, stopwords())
words <- strsplit(txt, " ")
word_freq <- table(words)
return (word_freq)
}
word_freq <- get_file_content()
wordcloud2(word_freq, shape="star", size=10)
Feature Based Grouping (Clusters)
cluster_data <- data %>% select(calories, rating, protein, fat) %>% scale
row.names(cluster_data) <- data[,1]
km.res <- kmeans(cluster_data, 5, nstart = 25)
fviz_cluster(km.res, data=cluster_data, palette = "jco", ggtheme = theme_minimal())

---
title: "Data Riders: EDA Mini-Project"
output:
  html_notebook:
    code_folding: hide
---

<style type="text/css">

h5 {
    background: rgba(108, 108, 108, 0.35);
    border-top: 1px solid #848484;
    border-bottom: 1px solid #848484;
    margin-top: 0px;
    padding: 10px 10px;
    font-weight: bold;
}
h5:hover {
  background: #ffeb3b;
}

h2 {
  background: #212121;
  color: white;
  padding: 8px 8px;
  margin-top: 0px;
  margin-bottom: 0px;
}

.container-part {
  border: 1px solid #a2a2a2;
  margin-bottom: 35px;
}

.main-container {
  max-width: 1300px;
  margin-left: auto;
  margin-right: auto;
}
</style>

```{r}
# Each team member would need to set their directory path to variable "project_dir"
# we will make sure everything else is relative to "project_dir"
project_dir <- "/home/admin-12/Documents/IMARTICUS/Data-Riders/EDA"
setwd(project_dir)

# Setup Packages
load_packages <- function () {
  # Imports
  packages <- c("VIM", "dplyr", "plotly", "psych", "corrplot", "cluster", "factoextra", "psych", "tm", "wordcloud2")
  installed_packages <- packages %in% rownames(installed.packages())
  if (any(installed_packages == FALSE)) {
    install.packages(packages[!installed_packages])
  }
  # Packages loading with suppressed messages
  suppressMessages(invisible(lapply(packages, library, character.only = TRUE)))
}
load_packages()

# Get the Raw Data from the file
read_file <- function () {
  data <- read.csv("data/cereals_data.csv")
  return (data)
}

# Handle missing values
handle_missing_values <- function (input) {
  output <- input
  output <- kNN(output)
  return (output)
}

# Clearn the data to make it good for processing
# eg. delete unwanted rows, change data types etc
data_processing <- function (input) {
  output <- input
  output <- handle_missing_values(output)

  # Set company names
  output$mfr_names <- as.character(output$mfr)
  output$mfr_names[output$mfr_names=="A"] <- "American Home Food Products"
  output$mfr_names[output$mfr_names=="G"] <- "General Mills"
  output$mfr_names[output$mfr_names=="K"] <- "Kelloggs"
  output$mfr_names[output$mfr_names=="N"] <- "Nabisco"
  output$mfr_names[output$mfr_names=="P"] <- "Post"
  output$mfr_names[output$mfr_names=="Q"] <- "Quaker Oats"
  output$mfr_names[output$mfr_names=="R"] <- "Ralston Purina"
  output$mfr_names_factor = as.factor(output$mfr_names)

  # Calorie Categories
  output <- within(output, {
    calories_category <- NA
    calories_category[calories>=50&calories<80] <- "L"
    calories_category[calories>=80&calories<110] <- "M"
    calories_category[calories>=110] <- "H"
  })

  # some data processing steps that would be required
  return (output)
}

# Get the Data Frame for the data
get_data <- function () {
  raw <- read_file()
  data <- data_processing(raw)
  return (data)
}

data <- get_data()
data_grp_mfr <- data %>% group_by(mfr_names_factor)
```

<!-- ---------- ---------- ---------- ---------- -->
<div class="container-part">
<h2>Part 1: Understanding Variables</h1>

<h5>Overall View</h5>

```{r echo=FALSE, warning=FALSE}
describe(data %>% select_if(is.numeric))
```

```{r}
draw_density_plot <- function (value_set, xlab) {
  skewness <- round(skew(value_set), 2)
  mean <- round(mean(value_set), 2)
  median <- round(median(value_set), 2)
  title <- paste(xlab, "Density Chart   |  ", "Skewness:", skewness)
  plot(density(value_set), ylab = "Probabilty Density", xlab = xlab, main=title,)
  abline(v=mean, col="red")
  abline(v=median, col="blue")
}
```

<h5>Distribution of Calories (in the Products)</h5>
```{r}
draw_density_plot(data$calories, "Calories")
```

<h5>Distribution of Ratings (for the Products)</h5>
```{r}
draw_density_plot(data$rating, "Rating")
```

</div>

<!-- ---------- ---------- ---------- ---------- -->
<div class="container-part">
<h2>Part 2: Data Insights</h1>

<h5>Distribution of Rating vs Calorie Category</h5>
```{r}
data %>% ggplot(aes(x = rating, fill = calories_category)) + geom_histogram() + scale_fill_brewer(palette = "Set1") +
        scale_x_continuous(name = "Ratings", expand = c(0,0)) +
        labs(fill = " Cal ", y ='Count', title = "Distribution of Rating vs Calorie Category", subtitle = "Higher the calories, Lower is the Rating") +
        theme_classic()
```

<h5>Relationship of one variable with another</h5>
```{r}
cor_result <- cor((data_grp_mfr %>% select_if(is.numeric))[-1])
corrplot(cor_result)
heatmap(cor_result)
```

```{r}
good_bad_corr <- function (cor_result_filter) {
  #cor_result_filter <-
  cor_result_filter_names <- names(cor_result_filter)
  names(cor_result_filter) <- NULL
  cor_result_df <- data.frame(cor_result_filter_names, cor_result_filter) %>% arrange(cor_result_filter)
  names(cor_result_df) <- c("Parameters", "Corellation")
  ggplot(cor_result_df, aes(x=Parameters, y=Corellation)) + geom_bar(stat = "identity") +
          theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))
}
```

<h5>Influencers for Ratings</h5>
```{r}
good_bad_corr(cor_result["rating",])
```

<h5>Influencers for Calories</h5>
```{r}
good_bad_corr(cor_result["calories",])
```

<h5>Whats you get in one Serving</h5>
```{r}
data %>% ggplot(aes(x = cups, fill = mfr_names)) +
        geom_histogram() +
        scale_x_continuous(name = "# of Cups", expand = c(0,0)) +
        labs(fill = "Manufacturer", title = "Distribution of Cups per Serving",
             subtitle = "different cups for servings", y="# of Products") +
        theme_classic() + scale_fill_brewer(palette="Dark2")
```

```{r}
data %>% ggplot(aes(x = weight, fill = mfr_names)) + geom_histogram() +
    scale_x_continuous(name = "Weight (in ounces)", expand = c(0,0)) +
    labs(fill = "Manufacturer", title = "Distribution of Weight per Serving", subtitle = "Weight per serving") +
    theme_classic() + scale_fill_brewer(palette="Dark2")
```

<h5>Best in the Market</h5>
```{r}
d <- data %>% arrange(rating) %>% head(4) %>% select(name, rating)
ggplot(data=d, aes(x=name, y=rating)) + geom_bar(stat = "identity") +
        labs(y="Ratings") + scale_fill_brewer(palette="Dark2") +
        theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))
```

</div>

<!-- ---------- ---------- ---------- ---------- -->
<div class="container-part">
<h2>Part 3: Analysis by company</h1>

```{r out.width="100%"}
data_grp_mfr %>% plot_ly(y= ~rating, x= ~mfr_names_factor, type = "box", color= ~mfr_names_factor) %>%
  layout(title="Company wise Rating Consistency", xaxis=list(title="Manufactures"), yaxis=list(title="Rating"))
```
</div>

<div class="container-part">
<h2>Part 4: Overall Picture</h1>

Word Cloud (error in markdown)
```{r}
get_file_content <- function () {
  txt <- readLines("/home/admin-12/Documents/IMARTICUS/Data-Riders/EDA/data/health.txt")
  txt <- paste(txt, collapse = " ")
  txt <- tolower(txt)
  txt <- gsub("[^a-zA-Z]", " ", txt)
  txt <- gsub("\\s+", " ", txt)
  txt <- removeWords(txt, stopwords())
  words <- strsplit(txt, " ")
  word_freq <- table(words)
  return (word_freq)
}
word_freq <- get_file_content()
wordcloud2(word_freq, shape="star", size=10)
```

<h5>Feature Based Grouping (Clusters)</h5>
```{r}
cluster_data <- data %>% select(calories, rating, protein, fat) %>% scale
row.names(cluster_data) <- data[,1]
km.res <- kmeans(cluster_data, 5, nstart = 25)
fviz_cluster(km.res, data=cluster_data, palette = "jco", ggtheme = theme_minimal())
```


